An investigation into a missense variant associated with cataracts in Puerto Ricans.
A prospective analysis of disease incidence and progression with genetic, clinical, and lifestyle risk factors.
The meeting, which will be held in Los Angeles October 25-29, provides a forum for the discussion of cutting-edge science in all areas of human genetics.
Highlights of this year’s agenda include symposia, booths, scientific sessions, and workshops, as well as a Presidential Symposium on recent developments in African genomics.
23andMe at ASHG
Below you’ll find a full list of our presentations and posters at ASHG.
In addition to our research contributions, we’ll also have a booth (#1248) where conference attendees can learn more about several of our initiatives – including our Postdoc Program and our Research Innovation Collaboration Program. We hope to see you there!
Wednesday, Oct. 26 from 3:00 – 4:45 P.M. PT
“A Rare Missense Variant of Large Effect is Associated with Cataract in Puerto Ricans”
Jing Shi, PhD, Scientist II, Statistical Genetics
Poster # PB3281
“Comparison of trans-ancestry meta- and mega-analyses of cis-eQTLs in whole blood and LCLs”
Priyanka Nandakumar, PhD, Scientist II, Statistical Genetics
Poster # PB3345
“Prospective analysis of disease incidence and progression with genetic, clinical and lifestyle risk factors”
Wei Wang, PhD, Scientist II, Statistical Genetics
Poster # PB3563
Thursday, Oct. 27 from 3:00 – 4:45 P.M. PT
“Advancing rare disease research through web-based recruitment: the 23andMe Systemic Sclerosis Research Study”
Katelyn Kukar, Associate Program Manager
Poster # PB1256
“The genetic architectures of gene expression in individuals of African and European ancestry: results and consequences of eQTL studies”
Kipper Fletez-Brant, PhD, Scientist II, Computational Biology
Poster # PB2700
“Burden testing of imputed rare variants to inform therapeutic hypotheses”
Zachary Fuller, PhD, Scientist I, Statistical Genetics
Poster # PB3330
“GWAS of pericarditis derived from a natural language processing model on self-reported free text data identifies a genome-wide significant association on chromosome 2q14.1”
Chris German, PhD, Scientist I, Statistical Genetics
Poster # PB3426